Various conventional computer-based analysis methods are employed to analyze complex data collected from sensors which are monitoring equipment or process operations. In one approach, sensor signal data is compared against stored or predetermined thresholds and, when the data signals exceed the thresholds, an “alert” is automatically generated for notifying a human of the situation. Thus, equipment or process monitoring requires human intervention only when a sensor data exceeds a corresponding threshold. Alerts are thus typically the goal of the entire process in an industrial or technological context. To assist in the accuracy of the analysis, it is well-known to “smooth” the data prior to analysis, to remove extraneous, spurious or transient data points (e.g. a start-up spike) prior to comparison, in an effort to make the auto-analysis more reliable. Conventional computerized methods then typically involve one or more of the following basic steps in a typical data analysis: a) Smooth data (remove noise and outliers); b) Select, determine or calculate baseline; c) Compare smoothed data against baseline; d) Mark alerts when a certain threshold is exceeded.
One example of a process to be monitored is the operational performance of a gas turbine engine, wherein performance data such as low compressor speed (NL), high compressor speed (NH), inter-turbine temperature (ITT), fuel flow (Wf), etc. are typically monitored and recorded during engine operation. This data is then analyzed to verify that the engine is running properly and to permit actual or potential maintenance situations to be detected.
Conventional data analysis methods of the type described, however, when used on complex data, typically suffer from large numbers of false alerts (e.g. if thresholds are set too close to normal operating levels, or alert conditions are otherwise improperly marked) and/or of large numbers of missed alerts (e.g. if the thresholds are set too expansively, or alert conditions are otherwise missed). While false alerts reduce the operator's or service technician's confidence in the trend detection process, missed or delayed alerts can result in serious maintenance issues downstream. Sources of error include the use of statistical smoothing techniques, such as 15-point rolling average and exponential smoothing, which make data “too smooth” by improperly removing critical data points.
Another problem encountered when doing data analysis of this type is the presence of noise in the engine parameters. This noise should be removed from the data because it is not generally symptomatic of an engine condition. The prior art includes the use of various techniques, such as the use of Fast Fourier Transforms (FFT) algorithms to remove high frequency noise. However, other types of noise, such as low frequency noise may cause an automatic analysis system to miscalculate alerts. For example, in the operation of a machine, the seasonal variation in environmental operating conditions may affect parametric data relating to temperature, humidity, etc., yet the prior art does not adequately account for such factors.
Still another problem is how to calculate an accurate baseline for use in analyzing engine performance data. In the prior art, a new (or recently overhauled) engine is assumed to work “perfectly”, so, typically the first dozen (or so) data points are taken as the baseline for the rest of the data, typically by using averaging and/or regression techniques to arrive at a “baseline” value. However, the prior art does not account for the fact that there may be an immediate deterioration in the data as parts and subassemblies may settle somewhat, causing “slippage” (actually or metaphorically) in the system which may cause a baseline calculated from these first data points to be inaccurate. The difficulty is, however, that the first data points are typically the “best” baseline data available, and so they should not simply be ignored indiscriminately. Therefore, an improved method of calculating a baseline for machine performance data would be desirable.
Some of these problems have been addressed without success by the prior art systems. Others of these problems have not even been recognized by the prior art. Accordingly, there is a need for improved methods of data analysis and trend detection.